Recommendations are one of the strongest ways that people search for and find what they want. Whether shopping for a camera or trying to find a restaurant, people rely on getting recommendations from a variety of sources such as friends, experts, and publications.
Research in computer-generated recommendations has been active for decades and has resulted in a large amount of publications and various approaches, as well as many working systems with a range of successes. Computer-generated recommendations can be based on the codification of expertise from various sources. This was evidenced in the 1970s and 80s through the glut of expert system-based applications. The problem with this approach is the effort and difficulty in capturing and maintaining expertise in a computer application.
An alternative approach that addresses some of the drawbacks of expert systems are machine learning systems where the computer application can improve its own performance by adapting itself based on past performance. With regards to recommendations, this approach is manifested in the form of collaborative filtering algorithms.
Collaborative filtering bases recommendations on the experiences of other users. When faced with a decision in a particular domain people often consult with friends who have experiences in that domain. Collaborative filtering is a way to build those recommendations across a larger group of users, beyond just friends and acquaintances. The approach takes as input the feedback of these users in the form of reviews, scores, ranks, votes, etc. This input data is analyzed to find patterns and correlations between users and items that translate into probabilities of success of the recommendations.
A well known personalized movie recommendation website that uses collaborative filtering is MovieLens (http://www.movielens.com). In this personalized movie recommendation service, thousands of users submit scores for movies based on how well they liked or disliked the movies. Recommendations for a specific user can be made by looking at movies that are similar to movies liked by the user. This provides the recommendations of the type, “people who like this movie also like the following movies.” For example, a user who is looking at the movie Jerry Maguire may get a movie recommendation for A Few Good Men. In this case “similar” is based on the analysis of scores across the entire user population who has scored movies that intersect with the movies scored by the specific user requesting recommendations.
As illustrated in the above example, existing systems in the area of collaborative filtering have focused on making recommendations based on users' input data within a single domain. Since the input data is gathered only from a single domain, for example movies, the recommendations can be generated for items within this domain. Furthermore, the quality of collaborative filtering recommendations is dependent on the amount of user input data that is available. But if there is little data available in one domain, the recommendations generated are less relevant and less confident.
Therefore, there is a need for a system and method that can address the problems of making recommendations based on data collected from a single domain. In particular, there is a need for a system and method for using user input data from multiple domains in making product or service recommendations.